Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.
OBJECTIVE To identify geographic areas in the United States where food animal veterinary services may be insufficient to meet increased needs associated with the US FDA's Veterinary Feed Directive.
DESIGN Cross-sectional study.
SAMPLE Data collected between 2010 and 2016 from the US Veterinary Medicine Loan Repayment Program, the National Animal Health Monitoring System Small-Scale US Livestock Operations Study, and the USDA's National Veterinary Accreditation Program.
PROCEDURES Each dataset was analyzed separately to identify geographic areas with greatest potential for veterinary shortages. Geographic information systems methods were used to identify co-occurrence among the datasets of counties with veterinary shortages.
RESULTS Analysis of the loan repayment program, Small-Scale Livestock Operations Study, and veterinary accreditation datasets revealed veterinary shortages in 314, 346, and 117 counties, respectively. Of the 3,140 counties in the United States during the study period, 728 (23.2%) counties were identified as veterinary shortage areas in at least 1 dataset. Specifically, 680 counties were identified as shortage areas in 1 dataset, 47 as shortage areas in 2 datasets, and 1 Arizona county as a shortage area in all 3 datasets. Arizona, Kentucky, Missouri, South Dakota, and Virginia had ≥ 3 counties identified as shortage areas in ≥ 2 datasets.
CONCLUSIONS AND CLINICAL RELEVANCE Many geographic areas were identified across the United States where food animal veterinary services may be inadequate to implement the Veterinary Feed Directive and meet other producer needs. This information can be used to assess the impact of federal regulations and programs and help understand the factors that influence access to food animal veterinary services in specific geographic areas.
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